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Machine Learning Challenges

Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers

  • Conference proceedings
  • © 2006
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Overview

Editors:
  1. Joaquin Quiñonero-Candela

    1. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

  2. Ido Dagan

    1. Bar Ilan University, Ramat Gan, Israel

  3. Bernardo Magnini

    1. ITC-IRST, Trento, Italy

  4. Florence d’Alché-Buc

    1. Université d’Evry-Val d’Essonne, IBISC CNRS FRE 2873 and GENPOLE, Evry, France

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3944)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: MLCW 2005.

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Table of contents (25 papers)

  1. Front Matter

    Download chapter PDF

  2. Evaluating Predictive Uncertainty Challenge

    • Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Fabian Sinz, Olivier Bousquet, Bernhard Schölkopf
    Pages 1-27

  3. Classification with Bayesian Neural Networks

    • Radford M. Neal
    Pages 28-32

  4. A Pragmatic Bayesian Approach to Predictive Uncertainty

    • Iain Murray, Edward Snelson
    Pages 33-40

  5. Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees

    • Nitesh V. Chawla
    Pages 41-55

  6. Estimating Predictive Variances with Kernel Ridge Regression

    • Gavin C. Cawley, Nicola L. C. Talbot, Olivier Chapelle
    Pages 56-77

  7. Competitive Associative Nets and Cross-Validation for Estimating Predictive Uncertainty on Regression Problems

    • Shuichi Kurogi, Miho Sawa, Shinya Tanaka
    Pages 78-94

  8. Lessons Learned in the Challenge: Making Predictions and Scoring Them

    • Jukka Kohonen, Jukka Suomela
    Pages 95-116

  9. The 2005 PASCAL Visual Object Classes Challenge

    • Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop et al.
    Pages 117-176

  10. The PASCAL Recognising Textual Entailment Challenge

    • Ido Dagan, Oren Glickman, Bernardo Magnini
    Pages 177-190

  11. Using Bleu-like Algorithms for the Automatic Recognition of Entailment

    • Diana Pérez, Enrique Alfonseca
    Pages 191-204

  12. What Syntax Can Contribute in the Entailment Task

    • Lucy Vanderwende, William B. Dolan
    Pages 205-216

  13. Combining Lexical Resources with Tree Edit Distance for Recognizing Textual Entailment

    • Milen Kouylekov, Bernardo Magnini
    Pages 217-230

  14. Textual Entailment Recognition Based on Dependency Analysis and WordNet

    • Jesús Herrera, Anselmo Peñas, Felisa Verdejo
    Pages 231-239

  15. Learning Textual Entailment on a Distance Feature Space

    • Maria Teresa Pazienza, Marco Pennacchiotti, Fabio Massimo Zanzotto
    Pages 240-260

  16. An Inference Model for Semantic Entailment in Natural Language

    • Rodrigo de Salvo Braz, Roxana Girju, Vasin Punyakanok, Dan Roth, Mark Sammons
    Pages 261-286

  17. A Lexical Alignment Model for Probabilistic Textual Entailment

    • Oren Glickman, Ido Dagan, Moshe Koppel
    Pages 287-298

  18. Textual Entailment Recognition Using Inversion Transduction Grammars

    • Dekai Wu
    Pages 299-308

  19. Evaluating Semantic Evaluations: How RTE Measures Up

    • Sam Bayer, John Burger, Lisa Ferro, John Henderson, Lynette Hirschman, Alex Yeh
    Pages 309-331

  20. Partial Predicate Argument Structure Matching for Entailment Determination

    • Alina Andreevskaia, Zhuoyan Li, Sabine Bergler
    Pages 332-343
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  1. Machine Learning Challenges

Editors and affiliations

  • Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Joaquin Quiñonero-Candela

  • Bar Ilan University, Ramat Gan, Israel

    Ido Dagan

  • ITC-IRST, Trento, Italy

    Bernardo Magnini

  • Université d’Evry-Val d’Essonne, IBISC CNRS FRE 2873 and GENPOLE, Evry, France

    Florence d’Alché-Buc

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